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Sound event detection using non-negative dictionaries learned from annotated overlapping events.

, and . WASPAA, page 1-4. IEEE, (2013)

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Low-Complexity Acoustic Scene Classification in DCASE 2022 Challenge., , , , , , , and . DCASE, Tampere University, (2022)PicSOM Experiments in TRECVID 2013., , , , , , , , and . TRECVID, National Institute of Standards and Technology (NIST), (2013)Audio context recognition using audio event histograms., , , and . EUSIPCO, page 1272-1276. IEEE, (2010)Adaptation of a speech recognizer for singing voice., and . EUSIPCO, page 1779-1783. IEEE, (2009)TUT database for acoustic scene classification and sound event detection., , and . EUSIPCO, page 1128-1132. IEEE, (2016)Analysis of acoustic-semantic relationship for diversely annotated real-world audio data., , and . ICASSP, page 813-817. IEEE, (2013)Acoustic Scene Classification in DCASE 2020 Challenge: Generalization Across Devices and Low Complexity Solutions., , and . DCASE, page 56-60. (2020)Joint Measurement of Localization and Detection of Sound Events., , , , and . WASPAA, page 333-337. IEEE, (2019)Assessment of human and machine performance in acoustic scene classification: Dcase 2016 case study., , and . WASPAA, page 319-323. IEEE, (2017)Sound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations., , , and . ICASSP, page 151-155. IEEE, (2015)